garage.tf.regressors.gaussian_mlp_regressor_model module

GaussianMLPRegressorModel.

class GaussianMLPRegressorModel(input_shape, output_dim, name='GaussianMLPRegressorModel', **kwargs)[source]

Bases: garage.tf.models.gaussian_mlp_model.GaussianMLPModel

GaussianMLPRegressor based on garage.tf.models.Model class.

This class can be used to perform regression by fitting a Gaussian distribution to the outputs.

Parameters:
  • input_shape (tuple[int]) – Input shape of the training data.
  • output_dim (int) – Output dimension of the model.
  • name (str) – Model name, also the variable scope.
  • hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
  • hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
  • hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
  • output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
  • output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
  • learn_std (bool) – Is std trainable.
  • init_std (float) – Initial value for std.
  • adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.
  • std_share_network (bool) – Boolean for whether mean and std share the same network.
  • std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
  • min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.
  • max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.
  • std_hidden_nonlinearity – Nonlinearity for each hidden layer in the std network.
  • std_output_nonlinearity – Nonlinearity for output layer in the std network.
  • std_parametrization (str) – How the std should be parametrized. There are a few options:
  • exp (-) – the logarithm of the std will be stored, and applied a exponential transformation
  • softplus (-) – the std will be computed as log(1+exp(x))
  • layer_normalization (bool) – Bool for using layer normalization or not.
network_output_spec()[source]

Network output spec.